
# Imports
import collections
import numpy as np
import pandas as pd
import seaborn as sns
from itertools import combinations


# Options to display full tables instead of ellipses
pd.set_option("display.max_columns",50)
pd.set_option("display.max_rows",5000)


# Set path to HL7 Gen Core dataset
GEN_CORE = '../hl7_oct_2020/GenCore.csv'


# These are all of the variables which appear to be a date in the Hl7 data
date_vars = ['Birth_Date',
             'Illness_Onset_Dt',
             'Illness_End_Dt_str',
             'Diag_Dt',
             'Hosp_Admit_Dt',
             'Hosp_Dis_Dt',
             'Invest_Start_Dt',
             'First_Elec_Submit_Dt',
             'Elect_Notif_CDC_Dt',
             'Report_Dt',
             'Earliest_Cnty_Dt',
             'Earliest_State_Dt',
             'MMWR_Year',
             'PHD_Notif_Dt',
             'Message_Dt',
             'msg_received_dttm',
             'mvps_datetime_created',
             'mvps_datetime_updated',
             'current_record_flag', # Here and below are not dates but still needed for groupby operations
             'Report_State_txt',
             'Condition_Code_txt']


core_dts = pd.read_csv(GEN_CORE, usecols = date_vars, dtype = str)


# Apply restrictions that we intend to apply before generating synthetic data

# Exclude rows where current_record_flag is not 'Y'
core_dts = core_dts.loc[core_dts['current_record_flag'] == 'Y  ', :]
core_dts = core_dts.drop(columns = 'current_record_flag')

# Exclude rows where report_dt is missing
#core_dts = core_dts.copy() # to avoid SettingWithCopyWarning
core_dts = core_dts.loc[np.logical_not(core_dts['Report_Dt'].isna()), :]


# Missingness "Heatmap" -- Missing is ivory-colored, completed is black
sns.set(rc={'figure.figsize':(16,32)})
sns.heatmap(core_dts.isnull(), cbar=False).figure.savefig("./figures/overall_missingness.png")


# Missing percentages by variable
core_dts.apply(lambda x: sum(x.isnull()) / core_dts.shape[0] * 100)

Birth_Date                8.176795
Illness_Onset_Dt         46.148770
Illness_End_Dt_str       99.107239
Diag_Dt                  31.553860
Hosp_Admit_Dt            91.378257
Hosp_Dis_Dt              93.590160
Condition_Code_txt        0.006365
Invest_Start_Dt          21.117208
First_Elec_Submit_Dt      0.000000
Elect_Notif_CDC_Dt        0.000000
Report_Dt                 0.000000
Earliest_Cnty_Dt         60.523010
Earliest_State_Dt        21.537012
MMWR_Year                 0.029281
PHD_Notif_Dt             10.479194
Report_State_txt          0.000000
Message_Dt                0.000000
msg_received_dttm         0.000000
mvps_datetime_created     0.000000
mvps_datetime_updated    89.705935
dtype: float64


# So, we can set aside these dates that are never missing (or very rarely missing)
not_missing = ['First_Elec_Submit_Dt',
               'Elect_Notif_CDC_Dt',
               'Report_Dt',
               'MMWR_Year',
               'Message_Dt',
               'msg_received_dttm',
               'mvps_datetime_created']


missing = list(set(date_vars) - set(not_missing) - set(['current_record_flag', 'Report_State_txt', 'Condition_Code_txt']))
missing

['Birth_Date',
 'Diag_Dt',
 'Illness_Onset_Dt',
 'Invest_Start_Dt',
 'Illness_End_Dt_str',
 'PHD_Notif_Dt',
 'Earliest_Cnty_Dt',
 'Earliest_State_Dt',
 'mvps_datetime_updated',
 'Hosp_Admit_Dt',
 'Hosp_Dis_Dt']


# Define groupbys

# By diagnosis
dx = core_dts.groupby('Condition_Code_txt')

# By jurisdiction
dj = core_dts.groupby('Report_State_txt')

# By both
dx_dj = core_dts.groupby(['Report_State_txt', 'Condition_Code_txt'])
dj_dx = core_dts.groupby(['Condition_Code_txt', 'Report_State_txt'])


# Missingness by condition
missing_by_condition = pd.concat([dx[y].apply(lambda x: sum(x.isnull()) / len(x) * 100) for y in missing], axis = 1)
missing_by_condition['sample_size'] = dx.size().values
missing_by_condition


# Export table
missing_by_condition.to_csv('./datasets/missing_by_condition.csv')


sns.set(rc={'figure.figsize':(16,48)})
sns.heatmap(missing_by_condition.drop(columns='sample_size'), cbar = False, cmap = "mako").figure.savefig("./figures/missing_by_condition.png")


# Missingness by jurisdiction
missing_by_jurisdiction = pd.concat([dj[y].apply(lambda x: sum(x.isnull()) / len(x) * 100) for y in missing], axis = 1)
missing_by_jurisdiction['sample_size'] = dj.size().values
missing_by_jurisdiction


# Export table
missing_by_jurisdiction.to_csv('./datasets/missing_by_jurisdiction.csv')


sns.set(rc={'figure.figsize':(16,20)})
sns.heatmap(missing_by_jurisdiction.drop(columns='sample_size'), cbar = False, cmap = "mako").figure.savefig("./figures/missing_by_jurisdiction.png")


missing_by_condition_and_jurisdiction = pd.concat([dx_dj[y].apply(lambda x: sum(x.isnull()) / len(x) * 100) for y in missing], axis = 1)
missing_by_condition_and_jurisdiction['sample_size'] = dx_dj.size().values
missing_by_condition_and_jurisdiction


# Export table
missing_by_condition_and_jurisdiction.to_csv('./datasets/missing_by_jurisdiction.csv')


sns.set(rc={'figure.figsize':(16,200)})
sns.heatmap(missing_by_condition_and_jurisdiction.drop(columns='sample_size'), cbar = False, cmap = "mako").figure.savefig("./figures/missing_by_condition_jurisdiction.png")


missing_by_jurisdiction_and_condition = pd.concat([dj_dx[y].apply(lambda x: sum(x.isnull()) / len(x) * 100) for y in missing], axis = 1)
missing_by_jurisdiction_and_condition['sample_size'] = dj_dx.size().values
missing_by_jurisdiction_and_condition


# Export table
missing_by_jurisdiction_and_condition.to_csv('./datasets/missing_by_jurisdiction_and_condition.csv')


sns.set(rc={'figure.figsize':(16,200)})
sns.heatmap(missing_by_jurisdiction_and_condition.drop(columns='sample_size'), cbar = False, cmap = "mako").figure.savefig("./figures/missing_by_jurisdiction_condition.png")


# Gather date tuples
date_tuples = list(combinations(list(set(date_vars) - set(['current_record_flag', 'Report_State_txt', 'Condition_Code_txt'])), 2))
date_tuples

[('Message_Dt', 'Birth_Date'),
 ('Message_Dt', 'Report_Dt'),
 ('Message_Dt', 'First_Elec_Submit_Dt'),
 ('Message_Dt', 'Illness_Onset_Dt'),
 ('Message_Dt', 'Invest_Start_Dt'),
 ('Message_Dt', 'Illness_End_Dt_str'),
 ('Message_Dt', 'PHD_Notif_Dt'),
 ('Message_Dt', 'Earliest_Cnty_Dt'),
 ('Message_Dt', 'mvps_datetime_created'),
 ('Message_Dt', 'Elect_Notif_CDC_Dt'),
 ('Message_Dt', 'Hosp_Admit_Dt'),
 ('Message_Dt', 'Diag_Dt'),
 ('Message_Dt', 'Hosp_Dis_Dt'),
 ('Message_Dt', 'msg_received_dttm'),
 ('Message_Dt', 'Earliest_State_Dt'),
 ('Message_Dt', 'mvps_datetime_updated'),
 ('Message_Dt', 'MMWR_Year'),
 ('Birth_Date', 'Report_Dt'),
 ('Birth_Date', 'First_Elec_Submit_Dt'),
 ('Birth_Date', 'Illness_Onset_Dt'),
 ('Birth_Date', 'Invest_Start_Dt'),
 ('Birth_Date', 'Illness_End_Dt_str'),
 ('Birth_Date', 'PHD_Notif_Dt'),
 ('Birth_Date', 'Earliest_Cnty_Dt'),
 ('Birth_Date', 'mvps_datetime_created'),
 ('Birth_Date', 'Elect_Notif_CDC_Dt'),
 ('Birth_Date', 'Hosp_Admit_Dt'),
 ('Birth_Date', 'Diag_Dt'),
 ('Birth_Date', 'Hosp_Dis_Dt'),
 ('Birth_Date', 'msg_received_dttm'),
 ('Birth_Date', 'Earliest_State_Dt'),
 ('Birth_Date', 'mvps_datetime_updated'),
 ('Birth_Date', 'MMWR_Year'),
 ('Report_Dt', 'First_Elec_Submit_Dt'),
 ('Report_Dt', 'Illness_Onset_Dt'),
 ('Report_Dt', 'Invest_Start_Dt'),
 ('Report_Dt', 'Illness_End_Dt_str'),
 ('Report_Dt', 'PHD_Notif_Dt'),
 ('Report_Dt', 'Earliest_Cnty_Dt'),
 ('Report_Dt', 'mvps_datetime_created'),
 ('Report_Dt', 'Elect_Notif_CDC_Dt'),
 ('Report_Dt', 'Hosp_Admit_Dt'),
 ('Report_Dt', 'Diag_Dt'),
 ('Report_Dt', 'Hosp_Dis_Dt'),
 ('Report_Dt', 'msg_received_dttm'),
 ('Report_Dt', 'Earliest_State_Dt'),
 ('Report_Dt', 'mvps_datetime_updated'),
 ('Report_Dt', 'MMWR_Year'),
 ('First_Elec_Submit_Dt', 'Illness_Onset_Dt'),
 ('First_Elec_Submit_Dt', 'Invest_Start_Dt'),
 ('First_Elec_Submit_Dt', 'Illness_End_Dt_str'),
 ('First_Elec_Submit_Dt', 'PHD_Notif_Dt'),
 ('First_Elec_Submit_Dt', 'Earliest_Cnty_Dt'),
 ('First_Elec_Submit_Dt', 'mvps_datetime_created'),
 ('First_Elec_Submit_Dt', 'Elect_Notif_CDC_Dt'),
 ('First_Elec_Submit_Dt', 'Hosp_Admit_Dt'),
 ('First_Elec_Submit_Dt', 'Diag_Dt'),
 ('First_Elec_Submit_Dt', 'Hosp_Dis_Dt'),
 ('First_Elec_Submit_Dt', 'msg_received_dttm'),
 ('First_Elec_Submit_Dt', 'Earliest_State_Dt'),
 ('First_Elec_Submit_Dt', 'mvps_datetime_updated'),
 ('First_Elec_Submit_Dt', 'MMWR_Year'),
 ('Illness_Onset_Dt', 'Invest_Start_Dt'),
 ('Illness_Onset_Dt', 'Illness_End_Dt_str'),
 ('Illness_Onset_Dt', 'PHD_Notif_Dt'),
 ('Illness_Onset_Dt', 'Earliest_Cnty_Dt'),
 ('Illness_Onset_Dt', 'mvps_datetime_created'),
 ('Illness_Onset_Dt', 'Elect_Notif_CDC_Dt'),
 ('Illness_Onset_Dt', 'Hosp_Admit_Dt'),
 ('Illness_Onset_Dt', 'Diag_Dt'),
 ('Illness_Onset_Dt', 'Hosp_Dis_Dt'),
 ('Illness_Onset_Dt', 'msg_received_dttm'),
 ('Illness_Onset_Dt', 'Earliest_State_Dt'),
 ('Illness_Onset_Dt', 'mvps_datetime_updated'),
 ('Illness_Onset_Dt', 'MMWR_Year'),
 ('Invest_Start_Dt', 'Illness_End_Dt_str'),
 ('Invest_Start_Dt', 'PHD_Notif_Dt'),
 ('Invest_Start_Dt', 'Earliest_Cnty_Dt'),
 ('Invest_Start_Dt', 'mvps_datetime_created'),
 ('Invest_Start_Dt', 'Elect_Notif_CDC_Dt'),
 ('Invest_Start_Dt', 'Hosp_Admit_Dt'),
 ('Invest_Start_Dt', 'Diag_Dt'),
 ('Invest_Start_Dt', 'Hosp_Dis_Dt'),
 ('Invest_Start_Dt', 'msg_received_dttm'),
 ('Invest_Start_Dt', 'Earliest_State_Dt'),
 ('Invest_Start_Dt', 'mvps_datetime_updated'),
 ('Invest_Start_Dt', 'MMWR_Year'),
 ('Illness_End_Dt_str', 'PHD_Notif_Dt'),
 ('Illness_End_Dt_str', 'Earliest_Cnty_Dt'),
 ('Illness_End_Dt_str', 'mvps_datetime_created'),
 ('Illness_End_Dt_str', 'Elect_Notif_CDC_Dt'),
 ('Illness_End_Dt_str', 'Hosp_Admit_Dt'),
 ('Illness_End_Dt_str', 'Diag_Dt'),
 ('Illness_End_Dt_str', 'Hosp_Dis_Dt'),
 ('Illness_End_Dt_str', 'msg_received_dttm'),
 ('Illness_End_Dt_str', 'Earliest_State_Dt'),
 ('Illness_End_Dt_str', 'mvps_datetime_updated'),
 ('Illness_End_Dt_str', 'MMWR_Year'),
 ('PHD_Notif_Dt', 'Earliest_Cnty_Dt'),
 ('PHD_Notif_Dt', 'mvps_datetime_created'),
 ('PHD_Notif_Dt', 'Elect_Notif_CDC_Dt'),
 ('PHD_Notif_Dt', 'Hosp_Admit_Dt'),
 ('PHD_Notif_Dt', 'Diag_Dt'),
 ('PHD_Notif_Dt', 'Hosp_Dis_Dt'),
 ('PHD_Notif_Dt', 'msg_received_dttm'),
 ('PHD_Notif_Dt', 'Earliest_State_Dt'),
 ('PHD_Notif_Dt', 'mvps_datetime_updated'),
 ('PHD_Notif_Dt', 'MMWR_Year'),
 ('Earliest_Cnty_Dt', 'mvps_datetime_created'),
 ('Earliest_Cnty_Dt', 'Elect_Notif_CDC_Dt'),
 ('Earliest_Cnty_Dt', 'Hosp_Admit_Dt'),
 ('Earliest_Cnty_Dt', 'Diag_Dt'),
 ('Earliest_Cnty_Dt', 'Hosp_Dis_Dt'),
 ('Earliest_Cnty_Dt', 'msg_received_dttm'),
 ('Earliest_Cnty_Dt', 'Earliest_State_Dt'),
 ('Earliest_Cnty_Dt', 'mvps_datetime_updated'),
 ('Earliest_Cnty_Dt', 'MMWR_Year'),
 ('mvps_datetime_created', 'Elect_Notif_CDC_Dt'),
 ('mvps_datetime_created', 'Hosp_Admit_Dt'),
 ('mvps_datetime_created', 'Diag_Dt'),
 ('mvps_datetime_created', 'Hosp_Dis_Dt'),
 ('mvps_datetime_created', 'msg_received_dttm'),
 ('mvps_datetime_created', 'Earliest_State_Dt'),
 ('mvps_datetime_created', 'mvps_datetime_updated'),
 ('mvps_datetime_created', 'MMWR_Year'),
 ('Elect_Notif_CDC_Dt', 'Hosp_Admit_Dt'),
 ('Elect_Notif_CDC_Dt', 'Diag_Dt'),
 ('Elect_Notif_CDC_Dt', 'Hosp_Dis_Dt'),
 ('Elect_Notif_CDC_Dt', 'msg_received_dttm'),
 ('Elect_Notif_CDC_Dt', 'Earliest_State_Dt'),
 ('Elect_Notif_CDC_Dt', 'mvps_datetime_updated'),
 ('Elect_Notif_CDC_Dt', 'MMWR_Year'),
 ('Hosp_Admit_Dt', 'Diag_Dt'),
 ('Hosp_Admit_Dt', 'Hosp_Dis_Dt'),
 ('Hosp_Admit_Dt', 'msg_received_dttm'),
 ('Hosp_Admit_Dt', 'Earliest_State_Dt'),
 ('Hosp_Admit_Dt', 'mvps_datetime_updated'),
 ('Hosp_Admit_Dt', 'MMWR_Year'),
 ('Diag_Dt', 'Hosp_Dis_Dt'),
 ('Diag_Dt', 'msg_received_dttm'),
 ('Diag_Dt', 'Earliest_State_Dt'),
 ('Diag_Dt', 'mvps_datetime_updated'),
 ('Diag_Dt', 'MMWR_Year'),
 ('Hosp_Dis_Dt', 'msg_received_dttm'),
 ('Hosp_Dis_Dt', 'Earliest_State_Dt'),
 ('Hosp_Dis_Dt', 'mvps_datetime_updated'),
 ('Hosp_Dis_Dt', 'MMWR_Year'),
 ('msg_received_dttm', 'Earliest_State_Dt'),
 ('msg_received_dttm', 'mvps_datetime_updated'),
 ('msg_received_dttm', 'MMWR_Year'),
 ('Earliest_State_Dt', 'mvps_datetime_updated'),
 ('Earliest_State_Dt', 'MMWR_Year'),
 ('mvps_datetime_updated', 'MMWR_Year')]


# Date parsing utility function for the function to follow
def dateParse(df, x):
    
    # It seems that illness_end_dt_str are formatted as YYYYMMDD, while other datetime variables are YYYY-MM-DD.
    # However, there are other peculiaries, such as illness_end_dt_str ending in .000 and datetime variables
    # requiring their time component to be stripped away, which is hanlded by the string slicing below.
    if x == 'Illness_End_Dt_str':
        return pd.to_datetime(df[x].str.slice(start = 0, stop = 8), errors='coerce', format = '%Y%m%d')
    else:
        return pd.to_datetime(df[x].str.slice(start = 0, stop = 10), errors='coerce', format = '%Y-%m-%d')


# Function to process a date tuple and assess how often both dates were present, and if they were, how often a < b
def processTuple(tup):
    
    # Unpack
    a, b = tup
       
    # Subset the data to complete cases (both a and b exist)
    sub = core_dts[[a,b]] 
    sub_complete = sub.loc[~(sub[a].isna() | sub[b].isna()), :]
    
    # Capture the sample size of this and the percentage of the data this represents
    complete_cases = sub_complete.shape[0]
    complete_case_pct = round(complete_cases / sub.shape[0] * 100,2)
    
    # Depending on the variable type, it needs to be converted to a date differently because the dates are in varying formats
    a_dates = dateParse(sub_complete, a)
    b_dates = dateParse(sub_complete, b)
    
    # Subtract and convert date differences to days
    a_b = (a_dates - b_dates) / np.timedelta64(1, 'D')
    
    # Gather statistics about this difference
    a_b_mean   = a_b.mean()
    a_b_std    = a_b.std()
    a_b_median = a_b.median()
    a_b_min    = a_b.min()
    a_b_max    = a_b.max()
    a_b_l95    = a_b.quantile(0.025)
    a_b_u95    = a_b.quantile(0.975)
    
    # Pass back a dataframe containing all difference information
    return pd.DataFrame(data = {'Date1':[a],
                                'Date2':[b],
                                'Complete_Cases':[complete_cases],
                                'Complete_Case_Pct':[complete_case_pct],
                                'Min':[a_b_min],
                                'Max':[a_b_max],
                                'Mean':[a_b_mean],
                                'Std':[a_b_std],
                                'Median':[a_b_median],
                                'L95':[a_b_l95],
                                'U95':[a_b_u95]})


# Example of use of this function on a single tuple
processTuple(('Illness_Onset_Dt','Hosp_Dis_Dt'))


# Call this on all date combination tuples
date_sequences = pd.concat([processTuple(x) for x in date_tuples])


# Show the distributions of pairwise date differences (Date1 - Date2)
# i.e. NEGATIVE differences imply Date1 came BEFORE Date2
#      POSITIVE differences imply Date1 came AFTER  Date2
date_sequences


# Export table
date_sequences.to_csv('./datasets/date_sequence_distributions.csv', index = False)

	Birth_Date	Diag_Dt	Illness_Onset_Dt	Invest_Start_Dt	Illness_End_Dt_str	PHD_Notif_Dt	Earliest_Cnty_Dt	Earliest_State_Dt	mvps_datetime_updated	Hosp_Admit_Dt	Hosp_Dis_Dt	sample_size
Report_State_txt
Alabama	0.000000	2.928870	2.928870	0.000000	84.518828	99.581590	86.192469	84.518828	100.000000	70.711297	81.589958	239
Alaska	0.000000	85.438972	18.736617	100.000000	100.000000	0.000000	100.000000	0.000000	99.892934	93.897216	96.145610	934
Arizona	0.000000	37.500000	62.500000	25.000000	100.000000	0.000000	0.000000	0.000000	100.000000	87.500000	100.000000	8
CT	0.059589	94.637005	66.511074	100.000000	100.000000	0.000000	100.000000	0.000000	94.537690	86.374019	88.310656	10069
California	0.262038	64.131418	56.851556	100.000000	100.000000	0.009311	0.009311	0.009311	91.424581	83.806864	85.986965	75180
Colorado	0.170960	99.937833	40.828379	22.446284	100.000000	0.000000	0.000000	0.000000	65.543770	83.129347	89.726852	25737
Delaware	0.000000	82.686308	85.961872	86.100520	98.249567	95.008666	99.948007	83.778163	84.090121	90.277296	90.849220	5770
Florida	0.000000	100.000000	100.000000	100.000000	100.000000	0.000000	0.000000	0.000000	100.000000	100.000000	100.000000	1
Georgia	0.817321	50.126768	37.316520	0.000000	100.000000	51.537897	100.000000	0.000000	80.014011	75.984121	84.480918	29976
Idaho	0.086308	20.689230	91.609642	0.012330	98.125886	3.458480	8.008138	9.204118	95.567474	99.118427	99.482153	16221
Illinois	0.061317	31.893063	0.000000	17.266893	100.000000	100.000000	0.000000	0.000000	95.507501	66.962351	75.129788	24463
Indiana	0.000000	48.915663	59.036145	0.000000	90.843373	0.000000	66.987952	69.638554	100.000000	78.554217	88.915663	415
Iowa	0.014969	52.578400	44.465235	0.000000	100.000000	0.000000	4.475713	0.000000	92.388294	91.310531	93.780406	13361
Kentucky	0.291121	24.599709	35.953421	0.000000	88.500728	0.000000	32.751092	39.883552	89.374090	61.280932	81.222707	687
Louisiana	0.067674	0.092282	99.624719	0.000000	100.000000	100.000000	99.996924	0.021532	91.537728	99.756990	99.787751	32509
Maine	0.035689	8.850821	8.708066	0.000000	91.541756	0.000000	0.000000	0.000000	47.287652	81.299072	88.579586	2802
Massachusetts	100.000000	53.124794	55.518870	22.334618	100.000000	0.000000	100.000000	100.000000	55.612265	89.050394	92.819089	76021
Michigan	0.088108	53.327264	67.559106	0.000000	100.000000	0.000000	100.000000	53.319628	98.448706	93.720227	94.997797	170245
Minnesota	0.024402	98.706686	8.735969	76.573939	100.000000	100.000000	100.000000	100.000000	73.792094	65.300146	68.106393	4098
Mississippi	0.156383	3.468866	71.751493	100.000000	100.000000	0.000000	100.000000	0.000000	61.373330	91.000853	94.242252	7034
Montana	0.197628	3.754941	13.438735	0.000000	51.976285	11.857708	1.778656	2.173913	79.644269	87.944664	92.490119	506
New Jersey	0.214018	53.196897	23.649010	0.006688	100.000000	100.000000	100.000000	100.000000	93.546014	70.358480	78.337346	14952
New York	0.110154	0.579195	54.248912	11.571467	100.000000	0.207871	0.399751	0.000000	97.189304	79.282224	84.020609	56285
North Carolina	0.008171	0.000000	0.000000	0.000000	100.000000	0.000000	100.000000	0.000000	99.525599	100.000000	100.000000	208052
Oregon	0.271459	13.417045	44.588913	0.005568	94.463624	0.005568	0.005568	0.000000	79.046134	96.450149	96.763371	71834
Pennsylvania	0.000000	18.662953	20.543175	0.139276	100.000000	0.000000	0.000000	0.000000	100.000000	51.880223	70.682451	1436
Rhode Island	0.000000	44.444444	11.111111	0.000000	88.888889	100.000000	100.000000	0.000000	100.000000	11.111111	11.111111	9
South Carolina	0.000000	13.732110	84.568807	78.524771	97.182875	0.000000	0.000000	0.000000	95.668502	96.455046	97.937615	81750
Tennessee	1.714286	47.142857	47.857143	0.000000	79.357143	0.000000	76.000000	64.928571	82.571429	83.571429	88.000000	1400
Utah	2.926829	61.951220	52.195122	11.707317	100.000000	0.000000	0.000000	0.000000	89.268293	100.000000	100.000000	205
Virginia	0.048619	85.239207	55.834306	0.009724	92.162583	8.031894	66.929210	88.807857	56.787242	85.054454	93.514197	10284
Wisconsin	0.000000	68.686869	41.414141	0.000000	100.000000	0.000000	16.161616	0.000000	96.969697	90.909091	93.939394	99

Date1	Date2	Complete_Cases	Complete_Case_Pct	Min	Max	Mean	Std	Median	L95	U95
Message_Dt	Birth_Date	865509	91.82	-5873.0	53818.0	12795.411752	6904.458343	10410.0	1539.000	28748.0
Message_Dt	Report_Dt	942582	100.00	-66438.0	27175.0	227.652506	443.613889	180.0	1.000	557.0
Message_Dt	First_Elec_Submit_Dt	942582	100.00	-1.0	4734.0	121.541082	190.572308	27.0	0.000	494.0
Message_Dt	Illness_Onset_Dt	507592	53.85	-66409.0	29545.0	226.756044	318.035043	207.0	8.000	546.0
Message_Dt	Invest_Start_Dt	743535	78.88	-3029.0	35679.0	240.339761	467.532811	193.0	1.000	554.0
Message_Dt	Illness_End_Dt_str	8415	0.89	-24204.0	6240.0	94.991086	311.323187	28.0	0.000	448.0
Message_Dt	PHD_Notif_Dt	843807	89.52	-66438.0	27175.0	207.591924	243.849562	193.0	1.000	525.0
Message_Dt	Earliest_Cnty_Dt	372103	39.48	-310.0	10942.0	176.502116	241.236516	120.0	1.000	574.0
Message_Dt	mvps_datetime_created	942582	100.00	-456.0	0.0	-8.513965	41.043621	0.0	-113.000	0.0
Message_Dt	Elect_Notif_CDC_Dt	942582	100.00	-456.0	509.0	2.234370	14.762324	0.0	0.000	11.0
Message_Dt	Hosp_Admit_Dt	81267	8.62	-32595.0	43258.0	173.808335	342.571274	98.0	6.000	634.0
Message_Dt	Diag_Dt	645161	68.45	-66409.0	36078.0	267.664985	526.342214	222.0	5.000	594.0
Message_Dt	Hosp_Dis_Dt	60418	6.41	-32758.0	43972.0	180.086946	549.172031	109.0	4.000	639.6
Message_Dt	msg_received_dttm	942582	100.00	-456.0	0.0	-0.833365	4.421492	0.0	-3.000	0.0
Message_Dt	Earliest_State_Dt	739578	78.46	-66438.0	23579.0	243.518912	479.917674	196.0	1.000	579.0
Message_Dt	mvps_datetime_updated	97030	10.29	-456.0	0.0	-1.274152	3.385192	-1.0	-3.000	0.0
Message_Dt	MMWR_Year	942306	99.97	-2.0	28237.0	398.384840	442.794407	406.0	78.000	784.0
Birth_Date	Report_Dt	865509	91.82	-88395.0	8312.0	-12551.821133	6915.757851	-10157.0	-28598.000	-1356.0
Birth_Date	First_Elec_Submit_Dt	865509	91.82	-53461.0	5873.0	-12666.467523	6910.648229	-10274.0	-28677.000	-1446.0
Birth_Date	Illness_Onset_Dt	473255	50.21	-73622.0	4316.0	-12584.516024	7220.404919	-10110.0	-28951.000	-819.0
Birth_Date	Invest_Start_Dt	683700	72.53	-53426.0	30937.0	-12412.607026	6697.944592	-10011.0	-28261.000	-2051.0
Birth_Date	Illness_End_Dt_str	8381	0.89	-53924.0	6081.0	-14477.837470	8747.551792	-14601.5	-29544.525	-304.0
Birth_Date	PHD_Notif_Dt	767044	81.38	-88395.0	8312.0	-11966.768138	6621.961218	-9658.0	-28090.000	-1429.0
Birth_Date	Earliest_Cnty_Dt	371574	39.42	-53426.0	165.0	-14090.568199	7658.970425	-12379.0	-29711.000	-834.0
Birth_Date	mvps_datetime_created	865509	91.82	-54080.0	5873.0	-12804.645178	6908.317199	-10415.0	-28762.000	-1566.0
Birth_Date	Elect_Notif_CDC_Dt	865509	91.82	-53818.0	5873.0	-12792.993699	6903.960727	-10409.0	-28744.000	-1531.0
Birth_Date	Hosp_Admit_Dt	72919	7.74	-42502.0	811.0	-18281.905841	8865.110050	-19823.0	-32502.000	-424.0
Birth_Date	Diag_Dt	609241	64.64	-73622.0	3603.0	-11937.178414	6477.296260	-9675.0	-27481.000	-1546.0
Birth_Date	Hosp_Dis_Dt	54940	5.83	-57152.0	808.0	-17985.147618	8821.452247	-19444.0	-32319.550	-435.0
Birth_Date	msg_received_dttm	865509	91.82	-53818.0	5873.0	-12796.306498	6904.483404	-10411.0	-28749.000	-1539.0
Birth_Date	Earliest_State_Dt	738683	78.37	-88395.0	5874.0	-12668.264760	6969.723157	-10304.0	-28561.000	-1233.0
Birth_Date	mvps_datetime_updated	63088	6.69	-43944.0	184.0	-15410.007244	8143.959531	-14288.0	-32032.825	-915.0
Birth_Date	MMWR_Year	865233	91.79	-53325.0	5981.0	-12373.836093	6915.152670	-9985.0	-28419.200	-1166.8
Report_Dt	First_Elec_Submit_Dt	942582	100.00	-27174.0	66438.0	-106.111424	352.585416	-28.0	-406.000	0.0
Report_Dt	Illness_Onset_Dt	507592	53.85	-66472.0	66481.0	11.089239	235.473089	5.0	0.000	49.0
Report_Dt	Invest_Start_Dt	743535	78.88	-27174.0	66472.0	-3.342519	130.235759	0.0	-28.000	0.0
Report_Dt	Illness_End_Dt_str	8415	0.89	-24298.0	6220.0	-11.445092	352.275508	-4.0	-32.000	12.0
Report_Dt	PHD_Notif_Dt	843807	89.52	-20205.0	3286.0	-0.349828	35.468538	0.0	-4.000	0.0
Report_Dt	Earliest_Cnty_Dt	372103	39.48	-20205.0	6544.0	-1.939527	83.793963	0.0	-9.000	0.0
Report_Dt	mvps_datetime_created	942582	100.00	-27175.0	66434.0	-236.166470	446.104187	-188.0	-633.000	-1.0
Report_Dt	Elect_Notif_CDC_Dt	942582	100.00	-27175.0	66438.0	-225.418136	443.967217	-176.0	-555.000	-1.0
Report_Dt	Hosp_Admit_Dt	81267	8.62	-32871.0	43114.0	7.426298	233.171151	3.0	-4.000	40.0
Report_Dt	Diag_Dt	645161	68.45	-66472.0	66481.0	7.367129	251.897877	3.0	-2.000	37.0
Report_Dt	Hosp_Dis_Dt	60418	6.41	-32876.0	43945.0	5.251916	485.966371	0.0	-18.000	39.0
Report_Dt	msg_received_dttm	942582	100.00	-27175.0	66434.0	-228.485871	443.887949	-181.0	-557.000	-1.0
Report_Dt	Earliest_State_Dt	739578	78.46	-20205.0	23489.0	-0.289480	65.785354	0.0	-6.000	2.0
Report_Dt	mvps_datetime_updated	97030	10.29	-27176.0	232.0	-149.245811	470.675239	-18.0	-570.000	-1.0
Report_Dt	MMWR_Year	942306	99.97	-27061.0	66642.0	170.798499	183.963135	156.0	11.000	353.0
First_Elec_Submit_Dt	Illness_Onset_Dt	507592	53.85	-66409.0	29265.0	79.726140	286.457694	34.0	4.000	381.0
First_Elec_Submit_Dt	Invest_Start_Dt	743535	78.88	-3399.0	35628.0	101.213616	362.722643	24.0	0.000	391.0
First_Elec_Submit_Dt	Illness_End_Dt_str	8415	0.89	-24295.0	6228.0	4.427858	276.542013	3.0	-12.000	47.0
First_Elec_Submit_Dt	PHD_Notif_Dt	843807	89.52	-66438.0	27174.0	90.686151	215.647364	31.0	0.000	396.0
First_Elec_Submit_Dt	Earliest_Cnty_Dt	372103	39.48	-404.0	9615.0	83.268213	208.802814	8.0	0.000	431.0
First_Elec_Submit_Dt	mvps_datetime_created	942582	100.00	-4734.0	0.0	-130.055047	197.186335	-38.0	-522.000	0.0
First_Elec_Submit_Dt	Elect_Notif_CDC_Dt	942582	100.00	-4734.0	509.0	-119.306712	191.009270	-18.0	-492.000	0.0
First_Elec_Submit_Dt	Hosp_Admit_Dt	81267	8.62	-32868.0	43162.0	89.592651	310.190722	18.0	1.000	493.0
First_Elec_Submit_Dt	Diag_Dt	645161	68.45	-66409.0	35814.0	113.566315	423.037276	36.0	2.000	436.0
First_Elec_Submit_Dt	Hosp_Dis_Dt	60418	6.41	-32875.0	43958.0	89.744492	527.432080	16.0	-6.000	484.0
First_Elec_Submit_Dt	msg_received_dttm	942582	100.00	-4734.0	0.0	-122.374447	190.661306	-28.0	-494.000	0.0
First_Elec_Submit_Dt	Earliest_State_Dt	739578	78.46	-66438.0	23575.0	100.231256	376.125285	28.0	0.000	420.0
First_Elec_Submit_Dt	mvps_datetime_updated	97030	10.29	-3399.0	0.0	-98.631361	194.548743	-8.0	-474.000	0.0
First_Elec_Submit_Dt	MMWR_Year	942306	99.97	-491.0	28237.0	276.846982	356.064723	252.0	36.000	486.0
Illness_Onset_Dt	Invest_Start_Dt	455749	48.35	-29236.0	66456.0	-13.555275	226.467449	-6.0	-62.000	-1.0
Illness_Onset_Dt	Illness_End_Dt_str	8377	0.89	-24300.0	6202.0	-15.487225	276.685721	-8.0	-46.000	-2.0
Illness_Onset_Dt	PHD_Notif_Dt	462100	49.02	-66481.0	66472.0	-11.388734	247.742043	-5.0	-48.000	0.0
Illness_Onset_Dt	Earliest_Cnty_Dt	165175	17.52	-29236.0	6601.0	-20.615795	233.409820	-8.0	-72.000	-2.0
Illness_Onset_Dt	mvps_datetime_created	507592	53.85	-29797.0	66409.0	-239.296041	323.534648	-215.0	-604.000	-9.0
Illness_Onset_Dt	Elect_Notif_CDC_Dt	507592	53.85	-29545.0	66409.0	-224.134427	318.423891	-203.0	-544.000	-8.0
Illness_Onset_Dt	Hosp_Admit_Dt	71824	7.62	-10617.0	43093.0	-4.735130	191.521652	-3.0	-28.000	2.0
Illness_Onset_Dt	Diag_Dt	415950	44.13	-32884.0	35792.0	-4.800509	201.207078	0.0	-25.000	0.0
Illness_Onset_Dt	Hosp_Dis_Dt	54502	5.78	-32882.0	43868.0	-9.456560	388.646150	-7.0	-37.000	-1.0
Illness_Onset_Dt	msg_received_dttm	507592	53.85	-29545.0	66409.0	-227.169940	318.019648	-207.0	-546.000	-8.0
Illness_Onset_Dt	Earliest_State_Dt	445259	47.24	-66481.0	66472.0	-11.601780	255.438227	-5.0	-49.000	0.0
Illness_Onset_Dt	mvps_datetime_updated	38519	4.09	-28175.0	209.0	-138.882759	291.237345	-32.0	-630.000	-7.0
Illness_Onset_Dt	MMWR_Year	507326	53.82	-29042.0	66748.0	164.789116	205.372885	161.0	7.000	347.0
Invest_Start_Dt	Illness_End_Dt_str	8340	0.88	-24296.0	6223.0	-2.384818	276.488784	1.0	-15.000	19.0
Invest_Start_Dt	PHD_Notif_Dt	657151	69.72	-66472.0	27174.0	2.215963	119.575624	0.0	0.000	24.0
Invest_Start_Dt	Earliest_Cnty_Dt	216294	22.95	-35623.0	6622.0	-1.419762	128.209535	0.0	0.000	8.0
Invest_Start_Dt	mvps_datetime_created	743535	78.88	-35695.0	3029.0	-249.691996	470.167788	-200.0	-646.000	-1.0
Invest_Start_Dt	Elect_Notif_CDC_Dt	743535	78.88	-35628.0	3029.0	-239.217218	467.693855	-192.0	-553.000	0.0
Invest_Start_Dt	Hosp_Admit_Dt	62685	6.65	-32871.0	43137.0	7.517077	249.549188	4.0	-1.000	33.0
Invest_Start_Dt	Diag_Dt	539754	57.26	-66456.0	35801.0	10.481527	240.787591	4.0	0.000	57.0
Invest_Start_Dt	Hosp_Dis_Dt	45055	4.78	-3892.0	43882.0	7.132083	465.673750	1.0	-15.000	28.0
Invest_Start_Dt	msg_received_dttm	743535	78.88	-35679.0	3029.0	-241.097780	467.814799	-195.0	-555.000	-1.0
Invest_Start_Dt	Earliest_State_Dt	565111	59.95	-66472.0	23489.0	3.076360	146.847142	0.0	0.000	31.0
Invest_Start_Dt	mvps_datetime_updated	72416	7.68	-10252.0	1457.0	-151.326779	510.863557	-16.0	-482.000	-1.0
Invest_Start_Dt	MMWR_Year	743259	78.85	-35305.0	28213.0	173.111396	156.396063	158.0	13.000	357.0
Illness_End_Dt_str	PHD_Notif_Dt	8242	0.87	-6220.0	24296.0	5.244509	355.297691	0.0	-19.000	15.0
Illness_End_Dt_str	Earliest_Cnty_Dt	7455	0.79	-6220.0	24296.0	3.132815	292.386963	0.0	-20.000	16.0
Illness_End_Dt_str	mvps_datetime_created	8415	0.89	-6241.0	24204.0	-97.027217	311.556962	-29.0	-448.000	0.0
Illness_End_Dt_str	Elect_Notif_CDC_Dt	8415	0.89	-6240.0	24204.0	-94.923342	311.363441	-28.0	-448.000	0.0
Illness_End_Dt_str	Hosp_Admit_Dt	1439	0.15	-324.0	426.0	6.618485	19.471522	4.0	0.000	25.0
Illness_End_Dt_str	Diag_Dt	6093	0.65	-364.0	24298.0	8.946159	313.420914	3.0	-10.000	21.0
Illness_End_Dt_str	Hosp_Dis_Dt	1360	0.14	-395.0	287.0	2.040441	17.638275	0.0	-9.000	17.0
Illness_End_Dt_str	msg_received_dttm	8415	0.89	-6241.0	24204.0	-95.376159	311.374488	-29.0	-448.000	0.0
Illness_End_Dt_str	Earliest_State_Dt	7369	0.78	-6220.0	24296.0	3.036916	294.127649	0.0	-20.000	16.0
Illness_End_Dt_str	mvps_datetime_updated	1317	0.14	-3417.0	256.0	-176.903569	192.258301	-154.0	-496.000	-2.0
Illness_End_Dt_str	MMWR_Year	8415	0.89	-5975.0	24409.0	175.004160	290.934729	160.0	16.000	357.0
PHD_Notif_Dt	Earliest_Cnty_Dt	346307	36.74	-20079.0	21.0	-1.130413	66.478740	0.0	0.000	0.0
PHD_Notif_Dt	mvps_datetime_created	843807	89.52	-27175.0	66434.0	-216.480519	248.857153	-200.0	-557.000	-1.0
PHD_Notif_Dt	Elect_Notif_CDC_Dt	843807	89.52	-27175.0	66438.0	-205.812336	244.142162	-191.0	-523.000	-1.0
PHD_Notif_Dt	Hosp_Admit_Dt	66273	7.03	-32871.0	43114.0	7.940881	254.232101	3.0	-2.000	39.0
PHD_Notif_Dt	Diag_Dt	586757	62.25	-66472.0	66481.0	6.989868	254.066467	3.0	0.000	31.0
PHD_Notif_Dt	Hosp_Dis_Dt	48953	5.19	-32868.0	43945.0	7.066718	517.812241	1.0	-16.000	37.0
PHD_Notif_Dt	msg_received_dttm	843807	89.52	-27175.0	66434.0	-208.330583	243.917889	-194.0	-525.000	-1.0
PHD_Notif_Dt	Earliest_State_Dt	665461	70.60	-3653.0	23489.0	0.282730	31.939674	0.0	0.000	3.0
PHD_Notif_Dt	mvps_datetime_updated	87652	9.30	-27176.0	232.0	-113.514090	196.704583	-18.0	-471.000	-1.0
PHD_Notif_Dt	MMWR_Year	843807	89.52	-27061.0	66642.0	171.572521	190.900494	157.0	12.000	354.0
Earliest_Cnty_Dt	mvps_datetime_created	372103	39.48	-10942.0	310.0	-192.589748	251.584091	-133.0	-672.000	-1.0
Earliest_Cnty_Dt	Elect_Notif_CDC_Dt	372103	39.48	-10942.0	310.0	-171.667635	241.966263	-106.0	-574.000	-1.0
Earliest_Cnty_Dt	Hosp_Admit_Dt	45320	4.81	-3270.0	22353.0	9.005847	132.426594	4.0	0.000	43.0
Earliest_Cnty_Dt	Diag_Dt	257094	27.28	-32866.0	24005.0	10.553488	215.981934	3.0	0.000	36.0
Earliest_Cnty_Dt	Hosp_Dis_Dt	34509	3.66	-32868.0	1121.0	2.220760	181.472959	1.0	-15.000	40.0
Earliest_Cnty_Dt	msg_received_dttm	372103	39.48	-10942.0	310.0	-177.445608	241.425399	-121.0	-575.000	-1.0
Earliest_Cnty_Dt	Earliest_State_Dt	369416	39.19	-3653.0	8257.0	0.688535	56.276363	0.0	-1.000	2.0
Earliest_Cnty_Dt	mvps_datetime_updated	40648	4.31	-6626.0	220.0	-184.483911	199.700770	-159.0	-492.000	-1.0
Earliest_Cnty_Dt	MMWR_Year	372103	39.48	-6511.0	20513.0	177.015799	172.227441	169.0	13.000	357.0
mvps_datetime_created	Elect_Notif_CDC_Dt	942582	100.00	0.0	509.0	10.748335	44.953436	0.0	0.000	174.0
mvps_datetime_created	Hosp_Admit_Dt	81267	8.62	-32595.0	43258.0	192.462672	351.406781	111.0	6.000	684.0
mvps_datetime_created	Diag_Dt	645161	68.45	-66409.0	36078.0	274.925809	527.639741	227.0	6.000	651.0
mvps_datetime_created	Hosp_Dis_Dt	60418	6.41	-32758.0	43972.0	201.151563	554.983238	123.0	4.000	685.0
mvps_datetime_created	msg_received_dttm	942582	100.00	0.0	321.0	7.680600	40.871492	0.0	0.000	108.0
mvps_datetime_created	Earliest_State_Dt	739578	78.46	-66434.0	23582.0	253.595968	482.285201	204.0	1.000	665.0
mvps_datetime_created	mvps_datetime_updated	97030	10.29	-3.0	0.0	-1.008472	0.877818	-1.0	-3.000	0.0
mvps_datetime_created	MMWR_Year	942306	99.97	0.0	28237.0	406.901293	445.703052	408.0	79.000	784.0
Elect_Notif_CDC_Dt	Hosp_Admit_Dt	81267	8.62	-32595.0	43257.0	169.102083	342.880404	87.0	5.000	634.0
Elect_Notif_CDC_Dt	Diag_Dt	645161	68.45	-66409.0	36077.0	266.027609	526.613198	220.0	4.000	594.0
Elect_Notif_CDC_Dt	Hosp_Dis_Dt	60418	6.41	-32758.0	43972.0	174.934075	549.492597	97.0	3.000	639.0
Elect_Notif_CDC_Dt	msg_received_dttm	942582	100.00	-509.0	0.0	-3.067735	15.360005	0.0	-21.000	0.0
Elect_Notif_CDC_Dt	Earliest_State_Dt	739578	78.46	-66438.0	23575.0	240.748828	480.407490	191.0	1.000	579.0
Elect_Notif_CDC_Dt	mvps_datetime_updated	97030	10.29	-509.0	0.0	-1.893579	12.773159	-1.0	-7.000	0.0
Elect_Notif_CDC_Dt	MMWR_Year	942306	99.97	-2.0	28237.0	396.149815	443.180017	406.0	78.000	784.0
Hosp_Admit_Dt	Diag_Dt	57575	6.11	-43102.0	32872.0	-1.651359	362.541689	-1.0	-14.000	4.0
Hosp_Admit_Dt	Hosp_Dis_Dt	60159	6.38	-43125.0	43946.0	-2.533196	527.935461	-3.0	-21.000	0.0
Hosp_Admit_Dt	msg_received_dttm	81267	8.62	-43258.0	32595.0	-174.301389	342.588790	-99.0	-634.000	-6.0
Hosp_Admit_Dt	Earliest_State_Dt	63534	6.74	-22353.0	23483.0	-7.960147	155.850882	-3.0	-43.000	1.0
Hosp_Admit_Dt	mvps_datetime_updated	10366	1.10	-22355.0	1.0	-162.175188	320.996187	-33.0	-682.875	-5.0
Hosp_Admit_Dt	MMWR_Year	81023	8.60	-42777.0	33069.0	175.450921	245.970534	177.0	13.000	351.0
Diag_Dt	Hosp_Dis_Dt	43128	4.58	-32872.0	43946.0	0.302177	589.321663	-2.0	-19.000	8.0
Diag_Dt	msg_received_dttm	645161	68.45	-36078.0	66409.0	-268.482246	526.635829	-222.0	-595.000	-5.0
Diag_Dt	Earliest_State_Dt	599724	63.63	-66481.0	66472.0	-7.781646	235.344172	-3.0	-40.000	0.0
Diag_Dt	mvps_datetime_updated	47567	5.05	-32902.0	348.0	-253.081023	661.672257	-161.0	-713.000	-4.0
Diag_Dt	MMWR_Year	644972	68.43	-35597.0	66748.0	166.791051	256.471901	164.0	6.000	350.0
Hosp_Dis_Dt	msg_received_dttm	60418	6.41	-43972.0	32758.0	-180.632008	549.179786	-109.0	-639.600	-4.0
Hosp_Dis_Dt	Earliest_State_Dt	48112	5.10	-11322.0	32868.0	-1.679179	196.684668	-1.0	-40.000	16.0
Hosp_Dis_Dt	mvps_datetime_updated	6255	0.66	-43888.0	10.0	-204.830828	614.590110	-98.0	-727.000	-4.0
Hosp_Dis_Dt	MMWR_Year	60273	6.39	-43863.0	33114.0	183.761498	490.709635	191.0	17.000	357.0
msg_received_dttm	Earliest_State_Dt	739578	78.46	-66434.0	23582.0	244.348393	480.212783	196.0	1.000	581.0
msg_received_dttm	mvps_datetime_updated	97030	10.29	-3.0	0.0	-1.008472	0.877818	-1.0	-3.000	0.0
msg_received_dttm	MMWR_Year	942306	99.97	0.0	28237.0	399.218444	443.108016	408.0	79.000	784.0
Earliest_State_Dt	mvps_datetime_updated	55231	5.86	-10252.0	232.0	-237.821948	577.376976	-158.0	-631.000	-1.0
Earliest_State_Dt	MMWR_Year	739578	78.46	-23272.0	66642.0	175.646091	207.673489	170.0	10.000	356.0
mvps_datetime_updated	MMWR_Year	97030	10.29	115.0	28239.0	283.710461	486.399793	117.0	115.000	873.0

Date Sequences and Missingness¶

Overall Missingness¶

Missingness by Condition¶

Missingness by Jurisdiction¶

Missingness by Condition and Jurisdiction¶

Missingness by Jurisdiction and Condition¶

Date Sequences¶

	Birth_Date	Diag_Dt	Illness_Onset_Dt	Invest_Start_Dt	Illness_End_Dt_str	PHD_Notif_Dt	Earliest_Cnty_Dt	Earliest_State_Dt	mvps_datetime_updated	Hosp_Admit_Dt	Hosp_Dis_Dt	sample_size
Condition_Code_txt
2019 Novel Coronavirus	0.000000	35.551664	29.597198	0.000000	100.000000	0.000000	15.236427	0.000000	49.970811	89.492119	94.220665	1713
2019 Novel Coronavirus (2019-nCoV)	85.868850	66.410126	61.668360	27.724311	99.184052	0.700879	86.840218	86.816307	47.701596	92.541395	96.785522	66916
2019 Novel Coronavirus (COVID-19)	0.000000	1.445783	6.024096	0.000000	42.650602	0.000000	1.686747	1.927711	88.433735	87.951807	92.530120	415
2019 Novel Coronavirus (nCoV)	0.000000	96.815946	99.326948	96.815946	99.948227	100.000000	100.000000	98.757442	77.763396	99.456381	99.870567	3863
2019-nCoV	0.386100	81.930502	77.683398	0.000000	90.617761	0.000000	47.837838	48.108108	92.471042	97.181467	98.648649	2590
ACUTE FLACCID MYELITIS	0.000000	100.000000	0.000000	0.000000	100.000000	0.000000	100.000000	0.000000	60.000000	0.000000	10.000000	10
AFM	0.000000	0.000000	0.000000	0.000000	100.000000	100.000000	0.000000	0.000000	100.000000	0.000000	0.000000	1
Acanthamoeba disease (excluding keratitis)	0.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	3
Acute Flaccid Myelitis	0.000000	100.000000	5.263158	26.315789	100.000000	0.000000	0.000000	0.000000	100.000000	21.052632	26.315789	19
Acute Flaccid Myelitis (AFM)	0.000000	69.230769	30.769231	0.000000	100.000000	0.000000	100.000000	69.230769	100.000000	38.461538	61.538462	13
Acute flaccid myelitis	0.000000	100.000000	100.000000	0.000000	100.000000	0.000000	0.000000	0.000000	100.000000	100.000000	100.000000	1
Anaplasma phagocytophilum	37.637726	42.089026	14.514470	35.448803	99.911855	34.493903	77.758190	71.676216	80.064639	74.247099	77.038343	6807
Anaplasma phagocytophilum infection	0.000000	85.714286	71.428571	0.000000	95.238095	52.380952	23.809524	90.476190	95.238095	80.952381	85.714286	21
Anaplasmosis	0.000000	0.000000	20.000000	0.000000	100.000000	60.000000	0.000000	0.000000	40.000000	80.000000	80.000000	5
Anaplasmosis phagocytophilum	0.000000	22.727273	0.000000	0.000000	100.000000	0.000000	27.272727	0.000000	100.000000	59.090909	68.181818	22
Animal Bites	0.000000	100.000000	0.000000	0.000000	100.000000	0.000000	0.000000	0.000000	100.000000	100.000000	100.000000	1
Anthrax	0.000000	0.000000	100.000000	0.000000	100.000000	0.000000	0.000000	0.000000	100.000000	0.000000	100.000000	1
BABESIOSIS	0.000000	0.148920	31.012658	15.487714	100.000000	0.372301	0.595681	0.000000	98.808637	56.850335	72.710350	2686
BOTULISM - INFANT	0.000000	0.000000	0.000000	0.000000	100.000000	0.000000	100.000000	0.000000	50.000000	0.000000	100.000000	2
BRUCELLOSIS	0.000000	0.000000	25.000000	6.250000	100.000000	0.000000	25.000000	0.000000	93.750000	43.750000	62.500000	16
Babesia	0.000000	10.526316	0.000000	0.000000	100.000000	0.000000	0.000000	0.000000	94.736842	94.736842	94.736842	19
Babesiosis	51.770956	37.750885	28.423849	23.494687	99.527745	20.188902	88.931523	71.517119	93.034238	66.499410	72.668241	3388
Bacillus anthracis	0.000000	0.000000	0.000000	0.000000	100.000000	0.000000	0.000000	0.000000	100.000000	100.000000	100.000000	2
Botulism - Infant	0.000000	33.333333	0.000000	0.000000	100.000000	0.000000	33.333333	0.000000	100.000000	16.666667	50.000000	6
Botulism - Other	0.000000	66.666667	100.000000	0.000000	100.000000	0.000000	100.000000	66.666667	100.000000	33.333333	100.000000	3
Brucella	0.000000	0.000000	0.000000	0.000000	100.000000	0.000000	0.000000	0.000000	84.615385	46.153846	69.230769	13
Brucellosis	1.807229	65.662651	19.277108	71.686747	99.397590	5.421687	20.481928	7.228916	98.192771	45.180723	57.228916	166
Brucellosis not temporally/spatially clustered	0.000000	60.000000	20.000000	0.000000	100.000000	0.000000	0.000000	0.000000	100.000000	60.000000	100.000000	5
CAMPYLOBACTERIOSIS	0.077477	0.209189	9.932595	7.158906	100.000000	1.131169	29.503370	0.000000	88.672813	69.226001	76.880762	12907
CHOLERA	0.000000	0.000000	0.000000	0.000000	100.000000	0.000000	33.333333	0.000000	100.000000	33.333333	33.333333	3
COVID-19	3.598201	47.226387	33.733133	0.000000	66.416792	0.000000	77.661169	72.563718	65.517241	89.655172	93.103448	667
COVID-19 (CORONA VIRUS)	2.866242	100.000000	55.944798	0.000000	100.000000	100.000000	100.000000	0.000000	100.000000	100.000000	100.000000	942
COVID-19 (CORONAVIRUS)	1.481429	98.321983	74.836762	0.000000	100.000000	98.132416	100.000000	0.000000	83.135575	98.911746	99.571719	14243
CP-CRE	0.000000	21.311475	47.540984	0.000000	100.000000	0.000000	100.000000	73.770492	100.000000	50.819672	75.409836	61
CP-CRE (Pre-2020)*	0.602410	39.759036	5.421687	0.000000	100.000000	0.000000	100.000000	39.759036	100.000000	100.000000	100.000000	166
CRYPTOSPORIDIOSIS	0.040800	0.407997	6.160751	4.243166	100.000000	2.488780	37.576499	0.000000	85.475316	69.808242	77.723378	2451
CYCLOSPORIASIS	0.000000	0.000000	0.000000	0.000000	100.000000	0.763359	100.000000	0.000000	74.809160	68.702290	84.732824	131
Campylobacter	0.097962	33.398642	29.604232	0.000000	90.327847	0.000000	45.637409	9.326019	98.360763	84.038662	85.481975	15312
Campylobacteriosis	10.770109	58.539732	47.780198	58.216121	99.079930	25.632945	27.798811	19.583748	93.273969	89.132596	91.486707	47279